For efficient management of bandwidth and maintenance of service quality, a communication or broadcasting transmitter should be able to adaptively adjust transmission data rate according to channel's conditions. Specifically, different channel conditions may lead to the transmitted signal modulated with different types of modulation techniques or a type of modulation technique with different levels, such as quadrature amplitude modulation (QAM) with different levels. Correspondingly, the receiver must be able to automatically recognize the type of modulation technique or the level of the modulation technique used in the transmitting side so that the demodulation process can demodulate the signal correctly. This associated technique required by the receiver is known as modulation recognition.
There have been numerous research reports and patents on modulation recognition since 1984. Among them, the developed algorithms can be categorized, based on their techniques or theories used, into five basic types: (1) pattern recognition, (2) decision theory, (3) second- and higher-order statistics, (4) neural networks, and (5) ad hoc. On the other hand, the aforementioned techniques can also be categorized, based on the type of the modulated signal that can be recognized, into only on digital modulation, and simultaneously on digital and analog modulations. Note that the types of digitally modulated signals include amplitude shift keying (ASK), phase shift keying (PSK), amplitude-phase shift keying (APSK), frequency shift keying (FSK), and QAM.
Specifically, Dominguez et al. (“A General Approach to the Automatic Classification of Radio-communication Signals,” Signal Processing, vol. 22, no. 3, Mar. 1991) disclosed a universal modulation recognition method by utilizing the histograms of the magnitude, phase and frequency of the received signals followed by a linear pattern recognition procedure, as shown in FIG. 1. This method could recognize almost all types of analog and/or digitally modulated signals. However, its performance is sensitive to phase error for the case of recognition of QAM signals.
QAM is a modulation technique that simultaneously places magnitude and phase of digital data onto a carrier. Because of its high spectrum efficiency, QAM is widely adopted by the standards of high-speed wired and wireless digital communication or broadcasting systems, such as V.29, V.34, DVB-C, DVB-T and ISDB-T, etc. Thereby, the QAM-related technologies, including modulation recognition technique, become the core technologies of designing receivers for communication and broadcasting systems with high-speed transmission and flexible bandwidth management allowed. However, recognition of the QAM signals with different levels (such as 64-QAM and 256-QAM) is far more difficult than that of the digitally modulated signals with different types of modulation techniques, such as those with ASK and PSK, or with PSK and QAM. The difficulty lies in that the modulated signals using different types of modulation techniques have significantly different features, whereas the differences between the features of the QAM signals for different levels are too small to discern easily.
For example, the method developed by Swami and Sadler (“Maximum-likelihood Modulation Classification for PSK/QAM,” IEEE Trans. Communications, vol. 48, no. 3, March 2000) uses the higher-order statistics of signals as the features. It can recognize the ASK, PSK, and QAM signals successfully, but fails to recognize the QAM signals of different levels due to the close similarity of the higher-order statistics of the QAM signals for different levels. In addition, with simultaneous utilization of the degrees of freedom of both the magnitude and phase, the QAM signals may appear more complicated compared to the ASK (simply utilizing the degree of freedom of magnitude) and PSK (simply utilizing the degree of freedom of phase) signals. For example, the constellations of higher-level QAMs (256-QAM or 1024-QAM) may have constellations overlapping those for lower-level QAM. This causes the confusion among the QAM signals of different levels and increases the difficulty of modulation recognition.
Another problem often in designing a receiver for a wired or wireless environment is the imperfect frequency (including phase) synchronization problem. The imperfect frequency synchronization comes from either the residual frequency offset or the phase offset for any practical frequency synchronization procedure, or the unpredictable phase noise created by some components, such as crystals and oscillators. Some frequency synchronization algorithms, such as the phase-lock loop (PLL) based carrier recovery algorithm, require the information about the modulation type of the received signals. This means that the modulation recognition must be done before performing the frequency synchronization. On the other hand, the receiver always suffers from some sorts of noise and interference. At the moment, the power ratio of desired signal v.s. noise is defined as signal-to-noise ratio (SNR). The lower the SNR, the more serious effect the noise and interference produce on received signals. Both the effect of imperfect frequency synchronization and the effect of noise and interference on the receiver may cause the failure of modulation recognition. In particular, the imperfect frequency synchronization will make some conventional modulation recognition algorithms assuming perfect frequency synchronization fail to recognize the type of received signals.
To eliminate the impact of the imperfect frequency synchronization, techniques similar to the maximum likelihood methods have been developed either by averaging the phases of received signals or by considering only the magnitude information of received signals. However, these methods are based on the assumption that the noise must be white Gaussian distributed with known variance, i.e., the SNR must be known. Although these algorithms have optimal performance when the assumption is satisfied, these algorithms still have two serious disadvantages. First, when actual signals are different from the assumed signal model, the performance of the algorithm will be significantly degraded. Second, the large amount of computational load becomes a burden on the hardware implementation. In addition, without cooperation between transmitting side and receiving side, the variance of the noise can only be estimated from the received signals at the receiver. When the employed modulation technique is unknown, additional efforts, such as multiple receivers or oversampling techniques, must be paid for the estimation of the noise variance. This essentially limits the design of receivers. Besides, there always exists an error between the estimated variance and the actual variance of the noise.
On the other hand, under the pre-requirements that (i) no additional information about the noise variance is needed, (ii) the performance is free from the imperfect frequency synchronization effect, and (iii) the computational complexity is low, the aforementioned modulation recognition method shown in FIG. 1 can be modified by using only the histograms of the magnitude of the received signals followed by a linear pattern recognition procedure. However, its performance will not meet the requirement of a reliable receiver, especially for the case of low SNR. Because of the wide applications of the QAM in communication and broadcasting systems and because of the aforementioned problems in existing modulation recognition techniques, a modulation recognition method that can recognize digitally modulated signals of multi-level magnitudes with low computational complexity is imperative in advancing the efficiency of communication systems.